Data Structures and Algorithms
Like Duolingo, but for Data Structures and Algorithms. Tomo turns the whole topic into a game you play five minutes a day, until it actually sticks.
For the part of you with thirty open tabs that never became anything.
87 levels across 9 sections, about 174 minutes end to end, roughly 35 days at five minutes a day. It moves through Thinking in Steps: How Computers Solve Problems, The Core Containers: Organizing Items in Lines, Controlled Traffic: Stacks and Queues, Finding and Sorting Data, Instant Lookups: Hashing and Hash Tables, Branching Out: Trees and Hierarchies, Connected Worlds: Graphs and Networks, Core Algorithmic Strategies, and Practical Engineering: Selecting and Tuning Data Structures. It starts from scratch.
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Key ideas in Data Structures and Algorithms
- An algorithm must describe every single step explicitly without assuming human intuition
- Given the same input deck, a valid algorithm must produce the exact same outcome every time
- Vague guidance like 'scan until you find a high card' fails because it lacks an exact stopping condition
- Computers follow instructions literally and cannot infer when to give up
- Without an explicit guard or boundary check, a repetitive loop repeats endlessly
- Unhandled edge cases result in infinite loops or runtime crashes
- Complex computational tasks must be reduced to single-action operations
- Comparison happens one element at a time before changing position
- Reaching a definitive negative answer requires exhausting the items step-by-step
- Tracing code involves updating a written table row-by-row as values change
- Evaluating branch conditions manually shows which path the execution takes
- Manual tracing reveals logic errors before executing code on a machine
- An algorithm must halt after a finite number of steps for every valid input
- An algorithm must return the intended output for all valid inputs, including edge cases
- An algorithm does not need to use the fewest lines of code or complete instantly to be fundamentally correct
- Running code on faster hardware makes bad algorithms look artificially fast
You've tried the other tabs
Thirty open tabs. Four facts you actually kept.
You watched. You nodded. By Sunday it was gone.
One answer, then back to scrolling.
Eight weeks. You meant to finish. You didn't.
Tomo gives Data Structures and Algorithms the Duolingo treatment: levels, streaks, and quick quizzes that test what you just learned. That game loop is what the tabs above never had, so it's the one you actually finish.
Here's what playing it feels like
A real question from this course. Take your best guess.
If you feed the exact same shuffled deck of cards into a valid sorting algorithm twice, what should happen?
Get it right to open this lesson and 86 more in the app.
Where Data Structures and Algorithms takes you
Understand how computers organize information and solve tough problems with lightning speed. Learn through intuitive real-world analogies, step-by-step logic, and practical trade-offs.
- 1
Thinking in Steps: How Computers Solve Problems
- What Is an Algorithm?
- Measuring Efficiency Without a Stopwatch
- 2
The Core Containers: Organizing Items in Lines
- Fixed-Size Arrays: The Row of Lockers
- Dynamic Arrays: The Expanding Locker Room
- Linked Lists: The Scavenger Hunt
- 3
Controlled Traffic: Stacks and Queues
- Stacks: The Cafeteria Tray Pile
- Queues and Deques: Fairness and Buffer Lines
- 4
Finding and Sorting Data
- Search Strategies: Linear vs. Binary
- Simple Sorts: Bubble, Selection, and Insertion
- Divide-and-Conquer Sorts: Merge and Quick Sort
- 5
Instant Lookups: Hashing and Hash Tables
- Hash Functions: The Instant Sorting Hat
- Hash Tables and Collision Handling
- 6
Branching Out: Trees and Hierarchies
- Tree Fundamentals and Binary Search Trees
- Heaps and Priority Queues
- 7
Connected Worlds: Graphs and Networks
- Graph Modeling: Nodes and Connections
- Exploring Graphs: Breadth-First and Depth-First Search
- Shortest Paths and Minimum Spanning Trees
- 8
Core Algorithmic Strategies
- Recursion: Solving Problems by Self-Reference
- The Greedy Approach: Making the Best Local Move
- Dynamic Programming: Remembering Past Work
- 9
Practical Engineering: Selecting and Tuning Data Structures
- Space-Time Trade-offs and Memory Realities
- Choosing the Right Tool for the Job
9 sections · 22 units · 87 levels. Built to play, not to enroll.
You pick the voice
Data Structures and Algorithms is taught in the The Professor style: clear, structured, thorough. Want a different feel? In the app you can spin up the same topic in any of Tomo's teaching styles. Same facts, totally different vibe.
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